Distributed deadlock detection and resolution in distributed databases

ABSTRACT

The subject technology performs a locking operation on a first set of keys by a first statement of a first transaction. The subject technology determines that a conflict occurred between the first statement and a second transaction. The subject technology determines that the second transaction has yet to complete after a predetermined period of time. The subject technology performs a deadlock detection process where the subject technology stores a key and value in a table indicating the first transaction and the second transaction, detects, based at least in part on a graph traversal of the table starting from the first transaction, a cycle between the first transaction and the second transaction, and determines that the first transaction is a youngest transaction in the detected cycle. The subject technology ceases execution of the first transaction in response to the first transaction being a youngest transaction in a detected cycle.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 63/201,487, filed Apr. 30, 2021, entitled “DISTRIBUTED DEADLOCKDETECTION AND RESOLUTION IN DISTRIBUTED DATABASES,” and the contents ofwhich is incorporated herein by reference in its entireties for allpurposes.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to a network-baseddatabase system or a cloud data platform and, more specifically, toprocessing concurrent transactions to enable transactional processing ina safe and performant manner (e.g., avoiding deadlock and starvation)within the database system.

BACKGROUND

Cloud-based data warehouses and other database systems or data platformssometimes provide support for transactional processing that enable suchsystems to perform operations that are not available through thebuilt-in, system-defined functions. However, for mitigating securityrisks, security mechanisms to ensure that user code running on suchsystems remain isolated are needed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageplatform, in accordance with some embodiments of the present disclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a computing environment conceptually illustrating an examplesoftware architecture for managing and executing concurrent transactionson a database system, which can be performed by a given execution nodeof the execution platform, in accordance with some embodiments of thepresent disclosure.

FIG. 5 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 6 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 8 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 9 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 10 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

In database systems, performing transactions on a given database can besupported. To facilitate that a given transaction is committed to atable, existing database systems can employ varying approaches includingOnline Transactional Processing (OLTP) techniques. As discussed herein,OLTP refers to a category of data processing that involvestransaction-oriented tasks. In an example, OLTP involves inserting,updating, and/or deleting varying amounts of data in a given database.OLTP can deal with large numbers of transactions by a large number ofusers. Increasingly, such transactions occur within and users areworking in a distributed and networked environment from varyinglocations and computing environments. Thus, it is also increasinglyimportant to ensure such transactions execute and complete in aconcurrent manner that protects the integrity and consistency of thedata in such a distributed environment.

As described herein, the subject technology provides concurrency controland isolation for executing a series of query statements (e.g., SQLstatements) within a transaction against a linearizable storage. Inparticular, the subject technology employs a concurrency controlmechanism that is a combination of a multi-version concurrency controlfor read operations (MVCC) and locking for write operations.Additionally, the subject technology implements a targeted isolationlevel (e.g., snapshot isolation), where each statement can executeagainst a different snapshot of a database, and write locks are helduntil a transaction commit.

The subject technology, in an embodiment, implements a two-leveltransaction hierarchy, where a top-level transaction corresponds to aSQL transaction, and a nested transaction corresponds to a SQL statementwithin the parent SQL transaction. A given nested transaction canperform read and write operations, and can perform a rollback andrestart execution zero or more times before succeeding. Upon transactioncommit, write operations can become visible, and write locks held byeach contained statement can be released.

Further, embodiments of the subject technology address deadlockdetection and resolution for databases. Advantageously, the subjecttechnology avoids false positives where only transactions involved in adeadlock will be aborted. This is helpful for users to find deadlocks intheir application code so that deadlocks can be fixed. In addition, thesubject technology implements embodiments of distributed deadlockdetection without a centralized transaction manager. In an example, thisis desirable for distributed databases, where each transaction isexecuted by a separate job, so that the coordination among differentjobs/nodes are minimized.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system in the example form of a network-based database system102, in accordance with some embodiments of the present disclosure. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 1. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of thecomputing environment 100 to facilitate additional functionality that isnot specifically described herein. In other embodiments, the computingenvironment may comprise another type of network-based database systemor a cloud data platform.

As shown, the computing environment 100 comprises the network-baseddatabase system 102 in communication with a cloud storage platform 104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage),and a cloud credential store provider 106. The network-based databasesystem 102 is a network-based system used for reporting and analysis ofintegrated data from one or more disparate sources including one or morestorage locations within the cloud storage platform 104. The cloudstorage platform 104 comprises a plurality of computing machines andprovides on-demand computer system resources such as data storage andcomputing power to the network-based database system 102.

The network-based database system 102 comprises a compute servicemanager 108, an execution platform 110, and one or more metadatadatabases 112. The network-based database system 102 hosts and providesdata reporting and analysis services to multiple client accounts.

The compute service manager 108 coordinates and manages operations ofthe network-based database system 102. The compute service manager 108also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (alsoreferred to as “virtual warehouses”). The compute service manager 108can support any number of client accounts such as end users providingdata storage and retrieval requests, system administrators managing thesystems and methods described herein, and other components/devices thatinteract with compute service manager 108.

The compute service manager 108 is also in communication with a clientdevice 114. The client device 114 corresponds to a user of one of themultiple client accounts supported by the network-based database system102. A user may utilize the client device 114 to submit data storage,retrieval, and analysis requests to the compute service manager 108.

The compute service manager 108 is also coupled to one or more metadatadatabases 112 that store metadata pertaining to various functions andaspects associated with the network-based database system 102 and itsusers. For example, a metadata database 112 may include a summary ofdata stored in remote data storage systems as well as data availablefrom a local cache. Additionally, a metadata database 112 may includeinformation regarding how data is organized in remote data storagesystems (e.g., the cloud storage platform 104) and the local caches.Information stored by a metadata database 112 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

As another example, a metadata database 112 can store one or morecredential objects 115. In general, a credential object 115 indicatesone or more security credentials to be retrieved from a remotecredential store. For example, the credential store provider 106maintains multiple remote credential stores 118-1 to 118-N. Each of theremote credential stores 118-1 to 118-N may be associated with a useraccount and may be used to store security credentials associated withthe user account. A credential object 115 can indicate one of moresecurity credentials to be retrieved by the compute service manager 108from one of the remote credential stores 118-1 to 118-N (e.g., for usein accessing data stored by the storage platform 104).

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the cloud storage platform104. The storage platform 104 comprises multiple data storage devices120-1 to 120-N. In some embodiments, the data storage devices 120-1 to120-N are cloud-based storage devices located in one or more geographiclocations. For example, the data storage devices 120-1 to 120-N may bepart of a public cloud infrastructure or a private cloud infrastructure.The data storage devices 120-1 to 120-N may be hard disk drives (HDDs),solid state drives (SSDs), storage clusters, Amazon S3™ storage systems,or any other data storage technology. Additionally, the cloud storageplatform 104 may include distributed file systems (such as HadoopDistributed File Systems (HDFS)), object storage systems, and the like.

As further shown, the storage platform 104 includes clock service 130which can be contacted to fetch a number that will be greater than anynumber previously returned, such as one that correlates to the currenttime. Clock service 130 is discussed further herein below with respectto embodiments of the subject system.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete cache files using a least recently used (LRU) policy andimplement an out of memory (OOM) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

The compute service manager 108, metadata database(s) 112, executionplatform 110, and storage platform 104, are shown in FIG. 1 asindividual discrete components. However, each of the compute servicemanager 108, metadata database(s) 112, execution platform 110, andstorage platform 104 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations). Additionally, each of the compute service manager 108,metadata database(s) 112, execution platform 110, and storage platform104 can be scaled up or down (independently of one another) depending onchanges to the requests received and the changing needs of thenetwork-based database system 102. Thus, in the described embodiments,the network-based database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the network-based database system 102processes multiple jobs determined by the compute service manager 108.These jobs are scheduled and managed by the compute service manager 108to determine when and how to execute the job. For example, the computeservice manager 108 may divide the job into multiple discrete tasks (ortransactions as discussed further herein) and may determine what data isneeded to execute each of the multiple discrete tasks. The computeservice manager 108 may assign each of the multiple discrete tasks toone or more nodes of the execution platform 110 to process the task. Thecompute service manager 108 may determine what data is needed to processa task and further determine which nodes within the execution platform110 are best suited to process the task. Some nodes may have alreadycached the data needed to process the task and, therefore, be a goodcandidate for processing the task. Metadata stored in a metadatadatabase 112 assists the compute service manager 108 in determiningwhich nodes in the execution platform 110 have already cached at least aportion of the data needed to process the task. One or more nodes in theexecution platform 110 process the task using data cached by the nodesand, if necessary, data retrieved from the cloud storage platform 104.It is desirable to retrieve as much data as possible from caches withinthe execution platform 110 because the retrieval speed is typically muchfaster than retrieving data from the cloud storage platform 104.

As shown in FIG. 1, the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices120-1 to 120-N in the cloud storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 120-1 to 120-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the cloud storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, the compute service manager 108 includesan access manager 202 and a credential management system 204 coupled toan access metadata database 206, which is an example of the metadatadatabase(s) 112. Access manager 202 handles authentication andauthorization tasks for the systems described herein. The credentialmanagement system 204 facilitates use of remote stored credentials(e.g., credentials stored in one of the remote credential stores 118-1to 118-N) to access external resources such as data resources in aremote storage device. As used herein, the remote storage devices mayalso be referred to as “persistent storage devices” or “shared storagedevices.” For example, the credential management system 204 may createand maintain remote credential store definitions and credential objects(e.g., in the access metadata database 206). A remote credential storedefinition identifies a remote credential store (e.g., one or more ofthe remote credential stores 118-1 to 118-N) and includes accessinformation to access security credentials from the remote credentialstore. A credential object identifies one or more security credentialsusing non-sensitive information (e.g., text strings) that are to beretrieved from a remote credential store for use in accessing anexternal resource. When a request invoking an external resource isreceived at run time, the credential management system 204 and accessmanager 202 use information stored in the access metadata database 206(e.g., a credential object and a credential store definition) toretrieve security credentials used to access the external resource froma remote credential store.

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata to process a received query (e.g., a data storage request or dataretrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 218 determines a priorityfor internal jobs that are scheduled by the compute service manager 108with other “outside” jobs such as user queries that may be scheduled byother systems in the database (e.g., the storage platform 104) but mayutilize the same processing resources in the execution platform 110. Insome embodiments, the job scheduler and coordinator 218 identifies orassigns particular nodes in the execution platform 110 to processparticular tasks. A virtual warehouse manager 220 manages the operationof multiple virtual warehouses implemented in the execution platform110. For example, the virtual warehouse manager 220 may generate queryplans for executing received queries.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local buffers(e.g., the buffers in execution platform 110). The configuration andmetadata manager 222 uses metadata to determine which data files need tobe accessed to retrieve data for processing a particular task or job. Amonitor and workload analyzer 224 oversee processes performed by thecompute service manager 108 and manages the distribution of tasks (e.g.,workload) across the virtual warehouses and execution nodes in theexecution platform 110. The monitor and workload analyzer 224 alsoredistributes tasks, as needed, based on changing workloads throughoutthe network-based database system 102 and may further redistribute tasksbased on a user (e.g., “external”) query workload that may also beprocessed by the execution platform 110. The configuration and metadatamanager 222 and the monitor and workload analyzer 224 are coupled to adata storage device 226. Data storage device 226 in FIG. 2 representsany data storage device within the network-based database system 102.For example, data storage device 226 may represent buffers in executionplatform 110, storage devices in storage platform 104, or any otherstorage device.

As described in embodiments herein, the compute service manager 108validates all communication from an execution platform (e.g., theexecution platform 110) to validate that the content and context of thatcommunication are consistent with the task(s) known to be assigned tothe execution platform. For example, an instance of the executionplatform executing a query A should not be allowed to request access todata-source D (e.g., data storage device 226) that is not relevant toquery A. Similarly, a given execution node (e.g., execution node 302-1may need to communicate with another execution node (e.g., executionnode 302-2), and should be disallowed from communicating with a thirdexecution node (e.g., execution node 312-1) and any such illicitcommunication can be recorded (e.g., in a log or other location). Also,the information stored on a given execution node is restricted to datarelevant to the current query and any other data is unusable, renderedso by destruction or encryption where the key is unavailable.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each include a data cache and a processor.The virtual warehouses can execute multiple tasks in parallel by usingthe multiple execution nodes. As discussed herein, the executionplatform 110 can add new virtual warehouses and drop existing virtualwarehouses in real-time based on the current processing needs of thesystems and users. This flexibility allows the execution platform 110 toquickly deploy large amounts of computing resources when needed withoutbeing forced to continue paying for those computing resources when theyare no longer needed. All virtual warehouses can access data from anydata storage device (e.g., any storage device in cloud storage platform104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 120-1 to 120-N shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 120-1 to120-N and, instead, can access data from any of the data storage devices120-1 to 120-N within the cloud storage platform 104. Similarly, each ofthe execution nodes shown in FIG. 3 can access data from any of the datastorage devices 120-1 to 120-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternative embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in cloudstorage platform 104. Thus, the caches reduce or eliminate thebottleneck problems occurring in platforms that consistently retrievedata from remote storage systems. Instead of repeatedly accessing datafrom the remote storage devices, the systems and methods describedherein access data from the caches in the execution nodes, which issignificantly faster and avoids the bottleneck problem discussed above.In some embodiments, the caches are implemented using high-speed memorydevices that provide fast access to the cached data. Each cache canstore data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a computing environment 400 conceptually illustrating anexample software architecture for managing and executing concurrenttransactions on a database system (e.g., the network-based databasesystem 102), which can be performed by a given execution node of theexecution platform 110, in accordance with some embodiments of thepresent disclosure. In an embodiment, a process flow is performed by atransaction manager that is configured to manage and executetransactions as described further herein.

As shown, the transaction manager 440 is included in the compute servicemanager 108. The transaction manager 440 receives a job 410 that may bedivided into one or more discrete transactions 420-425, e.g.,transaction 0, transaction 1, transaction 2, transaction 3, and so forththrough transaction (n). In an embodiment, each transaction includes oneor more tasks or operations (e.g., read operation, write operation,database statement, user defined function, and the like) to perform. Thetransaction manager 440 receives the job at 450 and determinestransactions at 452 that may be carried out to execute the job 410. Thetransaction manager 440 is configured to determine the one or morediscrete transactions, such as transaction 0, transaction 1, transaction2, transaction 3, and so forth, based on applicable rules and/orparameters. The transaction manager 440 assigns transactions at 454.

As further shown, the transaction manager 440 is configured toconcurrently process multiple jobs that can be performed by theexecution platform 110. In an example, the transaction manager 440 canreceive a second job 430 or a third job 435, each of which includerespective discrete transactions that are to be performed on theexecution platform 110. Each of the transactions may be executedconcurrently by the execution platform 110 in which different operationsare performed (e.g., a respective read operation or write operation areexecuted from each of the transactions by the execution platform 110).

In an implementation, the job 410, including the respective transactionstherein, is carried out by the transaction manager 440 which can performthe responsibilities of a query manager (e.g., processing querystatements and operations, and the like). As shown, the transactionmanager 440 may have multiple threads, including, for example,transaction manager threads 442A, 442B, 442C, and so forth. Thetransaction manager 440 may assign the job 410, including the multiplediscrete transactions, to a particular virtual warehouse of theexecution platform 110. Based on this assignment, the transactionmanager 440 can send the job 410, including the multiple discretetransactions, to the assigned virtual warehouse for execution.Alternatively, the transaction manager 440 can send a subset of thetransactions included in the job 410 for execution by the executionplatform 110.

In an embodiment, as described further herein, the transaction manager440 can perform operations to process transactions (e.g., OLTP) that maybe executing concurrently, while handling conflicts and avoidingstarvation of resources. Further, as described further herein, thetransaction manager 440 handles conflicts between multiple transactionsand concurrency issues that can arise when multiple transactions areexecuting in parallel on the execution platform 110. As further shown,the execution platform 110 communicates with the storage platform 104,which provides a distributed database (e.g., FoundationDB, and thelike), where data can be read and written in connection with performingthe transactions.

In an embodiment, the transaction manager 440 schedules and manages theexecution of transactions on behalf of a client account. The transactionmanager 440 may schedule any arbitrary SQL query included in a giventransaction. The transaction manager 440 may assume a role to schedulethe job 410 as if it is the client account rather than as an internalaccount or other special account. The transaction manager 440 may embodythe role of, for example, an account administrator or a role having the(smallest) scope necessary to complete the job 410. In an embodiment,the transaction manager 440 embodies the role that owns the object thatis the target of the job 410 (e.g. for a cluster, the table beingclustered is the target).

In an embodiment, the transaction manager 440 determines transactions at452 and assigns transactions at 454 that must be performed to fullyexecute the job 410. In an embodiment, the transaction manager 440assigns ordering constraints to any number of the one or more discretetransactions, where applicable. Depending on the constraints of the job410, the transaction manager 440 may determine that one or more ofmultiple discrete transactions must be serialized and executed in aparticular order.

In an embodiment, the transaction manager 440 generates a reportindicating when the job 410 is scheduled to be executed and how muchcomputing resources are estimated to be tied up executing the job 410.The transaction manager 440 may alert a client account when the job 410is being executed.

The subject technology provides concurrency control and isolation forexecuting transactions against (e.g., a series of SQL Statements withina SQL Transaction) against linearizable storage (e.g., a linearizablekey-value store). A transaction as referred to herein includes a groupof operations executed atomically. In an example, such transactions mayinclude read and write operations but can also include operations suchas increment, decrement, compare-and-swap, and the like. Further, it isappreciated that linearizable storage may include any type ofdistributed database (e.g., Apache HBase).

The following discussion relates to transactions in a given distributeddatabase system. In an example, the transaction manager 440 utilizes alinearizable storage, provided by the storage platform 104, for managingand processing transactions as described herein. In an embodiment, thetransaction manager 440 implements a read committed model for performingtransactions. As referred to herein, a read committed model can refer toa model that ensures that all read operations performed in a giventransaction sees a consistent snapshot of the database (e.g., reading alast set of committed values that existed when the read operationcommenced), and the transaction itself successfully commits only if noupdates that the transaction has made results in write-write conflictswith any concurrent transactions.

As discussed further herein, the transaction manager 440 implements atwo-level transaction hierarchy, where a top-level transactioncorresponds to a SQL transaction, and a nested transaction correspondsto a SQL statement within the parent SQL transaction. A given nestedtransaction can perform operations, such as reads and writes, and canperform a rollback and restart execution zero or more times beforesucceeding. Upon transaction commit, write operations can becomevisible, and write locks held by each contained statement can bereleased.

As mentioned before, the subject system provides concurrency control andisolation for executing a series of SQL Statements within a SQLTransaction against a linearizable storage. As discussed further herein,a transaction manager (e.g., transaction manager 440) is configured toprovide a concurrency control mechanism that can be understood as acombination of multi-version concurrency control for read operations(MVCC) and locking for write operations. The subject system providestechniques for read committed isolation where each statement may executeagainst a different snapshot of the database (e.g., the storage platform104), with write locks held until transaction commit.

In an embodiment, the linearizable storage as described herein enableseach operation to execute atomically between invocation and response. Asan example, such a linearizable key-value store ensures that operationsexecute in an atomic manner consistent with a “real-time” ordering ofthose operations e.g., when operation A completes before operation Bbegins, operation B should take effect after operation A. In the contextof a database, a first write operation to a row in the table must takeeffect before a second write or read operation to the same row in thetable if the second operation was issued after the first completed.

The examples described herein relate to linearizable storage such as alinearizable database, including, for example, NoSQL systems, and thelike. A given NoSQL database refers to a database that stores data in aformat other than a tabular format, and can store data differently thanin relational tables. Further, Uber's Schemaless is an example ofbuilding linearizable Key-Value storage via having a “key” and “value”column in a relational table. Other examples of linearizable databasesare: HBase, RocksDB, TiKV, Redis, Etcd.

Some examples of optimizations provided by the subject system includeutilizing restricted transactional capabilities offered by someembodiments of storage platform 104, such as FoundationDB, that can beleveraged to enable a more efficient transaction implementation. Forexample, in a write(/lock/delete) protocol, a write operation isperformed, and then a read operation is done to check for (1) any writeoperation that happened before the write request was submitted (2) anyother write operation was submitted concurrently with the writeoperation that was serialized before. The following example illustratesthe above:

-   -   T1 starts statement S1    -   S1 starts a FoundationDB Transaction, and uses its Read Version        as the Read Timestamp    -   S1 wishes to write object X, so it first reads object X as of        the Read Timestamp    -   Finding no conflicts, S1 writes X, using a timestamped operation        to embed the commit timestamp in the key and setting        IsCommitEmbedded.    -   S1 sets a read conflict range on the FoundationDB transaction        for all keys with a prefix of X    -   S1 writes a transaction status entry for ID, directly setting it        to committed.    -   T1 commits the FoundationDB Transaction.    -   If the transaction commits, then there were no concurrent        conflicting transactions.    -   If the transaction is aborted, then there was a concurrency        conflicting transaction for one of the writes that were done.        None of S1's writes, nor the transaction status entry will be        persisted. S1 must now restart in the slow path.

In an example, a “read version” refers to a “version” or state of thedatabase that corresponds to when a last operation was successfullycommitted to the database.

The following relates to a discussion of strict serializability. Whereaslinearizability makes a “real-time” ordering and atomicity promise aboutsingle operations, strict serializability makes a “real-time” orderingand atomicity promise about groups of operations. In an example, thegroup of operations is submitted incrementally over time, with aterminal “commit” command being issued. The strictly serializablestorage platform may employ techniques such as pessimistic lock-basedexclusion or an optimistic validation phase to enable thisfunctionality. In this example, the group of operations is referred toas a transaction as mentioned herein. The subject system can imposerestrictions on the transaction, such as the number, size, or durationof the operations, and always reject transactions that exceed theselimits.

In an embodiment, read operations may be optimized in the followingmanner. When reading with a given read timestamp, it may not be feasiblefor any transaction started after the read timestamp to commit beforethe read timestamp. Thus, if the Transaction ID is set to be the same asthe first statement's read timestamp, then instead of reading [X.0,X.inf], the subject system can read [X.0, X.readTimestamp].Consequently, this approach can make read operations for old orfrequently written data more efficient.

In an embodiment, the subject system implements a two-level transactionhierarchy, where the top-level transaction corresponds to a SQLTransaction, and the nested transaction (referred to as a“StatementContext”) corresponds to a SQL statement within the parent SQLTransaction. A given StatementContext performs read and write operationsand may be instructed to perform a rollback and restart execution zeroor more times before succeeding. In an example, transactions control thecollective visibility of all write operations from successfulstatements. Upon transaction commit, all write operations becomevisible, and all write locks held by each contained statement arereleased.

In an embodiment, each object key is associated with a stamp thatuniquely identifies a single execution attempt of a statement, which canbe by appending a three-part tuple of (Transaction ID, statementNumber,restartCount). The higher order component is the transaction identifierassigned to the SQL-level transaction. The statementNumber identifiesthe SQL statement within the SQL-level BEGIN/COMMIT block. The restartcount tracks which statement restart attempt generated this writeoperations. A StatementContext is instantiated with this stamp, andapplies it to all writes performed through the StatementContextinstance.

Stamping keys this way has a number of desirable properties. First, ifkey1<key2, then key1.suffix1<key2.suffix2, regardless of the values ofsuffix1 and suffix2. If key1==key2, then the transactionID component ofthe suffix allows us to resolve the commit status of the object todetermine its visibility to the statement. IftransactionID1==transactionID2, then Statement Number allows statementsto see writes performed by previous statements within the sametransaction. The restartCount component of the suffix enables the systemto detect and delete obsolete versions of the object that had been leftaround when a statement has to be restarted.

In a similar fashion each execution of a statement is given a three-partidentifier consisting of the statement's readTimestamp (RTS) and thecurrent values of statementNumber (SN) and restartCount (RC). Thisapproach ensures that each statement that is part of the execution of aSQL statement (or more generally a SQL Transaction), sees either datacommitted before the SQL statement started or by data written or updatedby the transaction itself.

In an embodiment, the transaction manager employs a Transaction StatusTable (TST) to keep track of committed and aborted transactions. The TSTis a persistent hashmap that maps Transaction ID to its metadata, mostnotably a list of finalized statement numbers and their final restartcount, and the commit outcome including the transaction's committimestamp (CTS). Transactions that are in progress do not exist in theTransaction Status Table. In an embodiment, the TST can be stored in thestorage platform 104, or within memory or cache of the executionplatform 110.

The following discussion relates to a read protocol that is utilized bythe transaction manager 440.

In an embodiment, the transaction manager 440 uses a read committedtransaction isolation level, and each statement may be run with adifferent read timestamp. In an example, the read request for a givenkey (or a range of keys) is implemented by executing a linearizablestorage read call for all keys with X as their prefix. The call returnsversions of X with their stamps and values. The read method returnseither the latest version of X made by a transaction that committedbefore the SQL statement started or which was written by the most recentstatement of the transaction itself that was not canceled (if any).

The following discussion relates to a write protocol that is utilized bythe transaction manager 440.

In an embodiment, the write protocol checks both for WW (write-write)conflicts and WW deadlocks. The following example describes a singletransaction and no conflicts. Assume that object X initially has a stampof TXN1.0.0 and was committed at timestamp 10. In the following example,it should be understood that the following transactional steps describedfurther below can be done within one transaction, and collectivelycommitted. On failure, or upon exceeding the limitations of theunderlying transactional system, the execution can fall back to issuingthe operations individually as described in further detail below.

T2 starts and creates S1 of StatementContext(ID=TXN2, StatementNumber=1, restartCount=0)

Assume that the constructor obtains a read timestamp from thelinearizable storage of 15 by contacting the clock service 130. Asmentioned before, the clock service 130 is a component of the storageplatform 104 which can be contacted to fetch a number that will begreater than any number previously returned, such as one that correlatesto the current time. In an embodiment, clock service 130 is providedseparately and is independently contactable from the linearizablestorage, or can be integrated into the linearizable storage such thatthe clock value may be inserted into a written value. The latteroperation will be referred to as a timestamped write.

To update value of X, the following sequence of actions is performed inan embodiment:

{

-   -   S1 does a linearizable storage write for X.TXN2.1.0 with a value        of 100    -   //The next step is for S1 to check for WW (write-write)        conflicts by checking whether there is    -   //another transaction that has updated X between the RTS and        S1's write.    -   S1 issues the range read [X.0, X.inf] to obtain the set all        versions of X and their stamps    -   The read returns [X.TXN1.0.0, X.TXN2.1.0].    -   S1 looks up TXN1 in the Transaction Status Table, finds a commit        timestamp of 10.    -   10 is earlier than our read timestamp of 15, so it is not a        conflict.    -   S1 ignores [X.TXN2.1.0] as it belongs to S1    -   //Assume for now, there were no conflicts detected    -   S1 finalizes, and records (statement number=1, restart count=0)        into the transaction    -   status table for TXN2        }        T2 commits. This will cause the Transaction Status Table record        to be updated in linearizable storage to reflect that TXN2 is        now committed and its commit timestamp of 20.

At this point there will be two versions of X, one stamped with TXN1.0.0and the other TXN2.1.0. Subsequent transactions that read X candetermine if this new version of X was written by a committedtransaction by reading the transaction status record, and determine theCTS of the transaction.

The write protocol for transaction T can now be stated.

In an implementation, each row (object) updated requires two separatelinearizable storage transactions:

-   -   1) The first linearizable storage transaction of T inserts a new        version of the object with its key X suffixed with three-part        suffix (T.ID, T.statementNumber, T.restartCount).    -   2) The second linearizable storage transaction issues a range        read with the prefix “X.” to obtain the SCT (set of conflicting        transactions). The result set is a list of committed or active        transactions that wrote (or are writing) new versions of X.

There are a number of possible distinct outcomes to this linearizablestorage read call that are evaluated in the following order:

-   -   1) SCT is empty in which case T is trivially allowed to proceed.    -   2) SCT is not empty, but for all Ti in SCT, Ti has committed        before T's read timestamp, and thus are not WW (write-write)        conflicts. T may proceed.    -   3) SCT is not empty; for all Ti in SCT, Ti is committed; and        there exists a Ti in SCT, such that its CTN is greater than T's        read timestamp. T is permitted to restart without delay.    -   4) SCT is not empty, and for one or more Ti in SCT, Ti has not        yet committed or aborted. T must wait for all transactions in        SCT to complete before restarting the current statement.    -   5) SCT is not empty, and for one or more Ti in SCT,        Ti.TransactionID is the same as our own transaction ID, and        Ti.StatementCount is less than our current statement count. This        means that currently the lock is held, as a previous statement        took it and successfully finished its execution. T may proceed.    -   6) SCT is not empty, and for one or more Ti in SCT,        TI.TransactionID is the same as our own transaction ID,        Ti.StatementCount is the same as our own StatementCount, and        Ti.RestartCount is less than our own restart count. This is a        lock from a previous execution of our own transaction, thus T        holds the lock on this row, and T may proceed.

For all cases, the object (X.Stamp, Value) will be left in the database(e.g., the storage platform 104). For (3) and (4) which requirerestarts, the object is left to serve as a write lock. In general, alltentative writes for an object X will form a queue of write locks. (5)and (6) illustrate the cases where previously left write locks allowsubsequent statements or restarts of a statement to recognize that theyalready hold the lock that they wish to take.

The following discussion describes an example that illustrates awrite-write (WW) conflict. A write-write conflict, which is alsounderstood as overwriting uncommitted data, refers to a computationalanomaly associated with interleaved execution of transactions. Tosimplify the example, stamps are omitted. Assume that before either T1or T2 starts that object X has a value of 500, a stamp of TXN1.0.0, anda CTN of 10.

T1 starts and gets a read timestamp of 15

T2 starts and gets a read timestamp of 20

T2 writes (key=X.T2, value=100)

T2 issues a linearizable storage read with range [X.0, X.Inf]. The setSCT will be empty so T2 continues

T1 writes (key=X.T1, value=50)

T1 issues a linearizable storage read with range [X.0, X.Inf]. The setSCT will contain T2 so T1 must restart

T2 successfully commits. T1's CTN for X will be >20. Assume it is 21

After waiting until T2 either commits or aborts, T1 restarts thestatement with a read TS>21.

The following discussion relates to a delete protocol utilized by thetransaction manager 440.

In an embodiment, delete operations are implemented as a write of asentinel tombstone value; otherwise, delete operations employ the sameprotocol as write operations. When a read operation determines that themost recently committed key is a tombstone, it considers that key to benon-existent.

The following discussion relates to a lock protocol utilized by thetransaction manager 440.

To support a query statement of SELECT . . . FOR UPDATE, the transactionmanager API offers StatementContext::lock(Key), which allows rows to belocked without writing a value to them. The implementation of lock( )follows the write protocol, except that it writes a special sentinelvalue to indicate the absence of a value (distinct from SQL NULL). ASELECT . . . FOR UPDATE statement may also be forced to restart severaltimes before the statement finishes successfully. Once it does,subsequent statements in the transaction will recognize the existence ofthis key as an indication that they hold the lock (in accordance withcases (5) and (6) above). All reads can ignore the key as a write.

The following discussion relates to determining whether to commit,abort, or restart a given transaction which can be determined by thetransaction manager 440.

When a transaction finishes its execution, it will either have an emptySCT, indicating that the commit can proceed, or an SCT with one or moreconflicting transactions, indicating that the transaction will need torestart.

When a statement is restarted, all writes stamped with a lowerrestartCount are left in the database (e.g., the storage platform 104)as provisional write locks for the next execution. The next execution ofthe statement might write a different set of keys. The set differencebetween the first and second execution form a set of orphaned writesthat must be removed and never become visible. The statement itself maynot be relied upon to always be able to clean up its own orphanedwrites, as in the event of a process crash, the location of the previouswrites will have been forgotten. Finalizing statements and recording therestart count of the successful execution promises that only the resultsof one execution will ever become visible, and permits orphaned writesto be lazily cleaned up.

A transaction is committed, and all of its writes made visible, byinserting its Transaction ID into the Transaction Status Table. Thecommit timestamp is filled in by the clock service 130 or directly bythe distributed database (e.g., FoundationDB), such that it is higherthan any previously assigned read or commit timestamps. All writes musthave completed before a statement may be finalized, and all statementsmust be finalized before the transaction may be committed.

A transaction is aborted by inserting its Transaction ID into theTransaction Status Table, with its transaction outcome set as aborted.The list of finalized statements and their restart counts will be resetto an empty list. The insertion into the Transaction Status Table willmake the abort outcome visible to all conflicting transactions, and allwrites performed by finalized statements may be proactively or lazilyremoved from the database (e.g., the storage platform 104).

When a statement tries to finalize with a non-empty SCT, it waits forcommit outcomes to be persisted to the Transaction Status Table for allconflicting transactions. Once all conflicting transactions havecommitted or aborted, then the transaction will begin its restartattempt.

The following discussion relates to an API (e.g., the transactionmanager API as referred to below) that can be utilized (e.g., by a givenclient device) to send commands and requests to the transaction manager440.

A SQL transaction contains a sequence of one or more SQL statements.Each SQL statement is executed as a nested transaction, as implementedby the transaction manager StatementContext class. Each transactionmanager statement itself is executed as one or more databasetransactions.

In an embodiment, the transaction manager API is divided into twoparts: 1) the data layer, which provides a read and write API to thetransaction execution processes; and 2) the transaction layer, whichprovides, to the compute service manager 108, an API to orchestrate thetransaction lifecycle. In an implementation, transactions operate at aREAD COMMITTED isolation level and implement MVCC on top of thedistributed database (e.g., storage platform 104) to avoid taking anyread locks.

Consider the following example SQL query:

Update emp.Salary=emp.Salary*1.1 where emp.Dept=“shoe”;

In an example, an instance of the StatementContext class will be createdto execute this SQL statement. The constructor contacts the linearizablestorage transaction manager to begin a linearizable storage transactionand obtain a linearizable storage STN which is then stored in thereadTimestamp variable.

The Update operation then executes across any number of execution nodes,all using the same StatementContext instance. In an example, a functionrangeRead( ) will be used to scan the base table, or an index on Dept,for the tuples to update. A series of write( ) calls will be made toupdate the salary of all matching employees.

A call to finalize( ) will return CONFLICT if the statement encounteredany conflicts during its execution, to indicate that re-execution isneeded. The key to restarts making progress is that the first executionof the statement will have the side effect of, in effect, setting writelocks on the objects being updated. This ensures that when the statementis re-executed the necessary writes locks have already been obtained andthe statement will generally (but not always).

Next, consider an example illustrating Write-Write conflicts between 3transactions:

T1 starts S1 with timestamp 10

T2 starts S2 with timestamp 20

T3 starts S3 with timestamp 30

S1 writes X

S2 writes Y

S3 writes Z

S1 writes Y, and notes the conflict with T2

S2 writes Z, and notes the conflict with T3

S3 writes X, and notes the conflict with T1

In this case described above, three transactions are involved in adeadlock. Each statement believes that it must restart and wait for theexecution of the previous transaction to finish. No transaction has thecomplete information to know that it is involved in a deadlock.

Thus, when a statement fails to finalize due to conflicts, it insteadwrites its conflict set into the database (e.g., the storage platform104). These conflict sets may be read by all other transactions,allowing them to detect a cycle in the waits-for graph, indicating thatthey're involved in a deadlock.

In database systems, a deadlock can refer to a situation where two ormore transactions are waiting for one another to give up locks. As anexample, deadlocks can be handled by deadlock detection or prevention insome embodiments. The following discussion relates to example mechanismsfor handling deadlocks utilizing distributed approaches that do notrequire a centralized deadlock handling component or implementation. Forexample, in an implementation, a particular execution node, (e.g.,execution node 302-1 and the like) in the execution platform 110 canperform at least some of the following operations described below.

Deadlock detection: A basic idea of deadlock detection is to detect adeadlock after the deadlock occurs such that that a particulartransaction can be aborted. This can be done by finding cycles in await-for graph. Depending on how deadlock detection is performed,deadlock detection can be classified as:

-   -   Online detection: whenever a transaction wishes to acquire a        lock, it adds an edge to the wait-for graph. The transaction is        aborted if this new edge will cause a cycle.    -   Offline detection: the system periodically collects the pending        lock requests from all transactions to construct a wait-for        graph and perform cycle detection.        Deadlock prevention: A basic idea of deadlock prevention is to        enforce some restrictions on locking so that deadlocks can never        happen. Example techniques include:    -   Timeout: a transaction is assumed to be involved in a deadlock        if its lock request cannot be granted after a certain time        period, e.g., 5 seconds.    -   Non-blocking 2PL: whenever a conflict happens, a transaction is        aborted immediately.    -   Wait-die: when a transaction Ti requests a lock that is held by        Tj, Ti is only allowed to wait if Ti is older than Tj. Otherwise        Ti is aborted immediately.    -   Wound-wait: when a transaction Ti requests a lock that is held        by Tj, Tj is aborted if Ti has a higher priority than Tj.        Otherwise, Ti will wait.

In embodiments, the subject technology implements a distributed database(e.g., storage platform 104) for executing distributed transactions, andutilizes locking for concurrency control where any deadlocks are handledin a distributed manner by a particular execution node executing aparticular transaction (e.g., execution node 302-1 and the like).

In some embodiments, the subject technology provides the following:

-   -   No false deadlocks: Deadlocks generally represent some bugs in        the user's application code. By providing accurate and        informative deadlock information, embodiments of the subject        technology enables a user to fix these deadlocks.    -   Distributed/decentralized deadlock handling: transaction manager        440 is designed for executing distributed transactions in the        cloud. In an embodiment, the transaction manager 440 creates one        job (with one or more execution node workers) to execute a        transaction. It can be desirable that each transaction handles        deadlocks independently without requiring a centralized        transaction manager.

The following discussion describes a deadlock detection and resolutionprotocol for the subject technology to meet the two aforementionedrequirements. In order to meet the goal of no false deadlocks, thesubject technology performs deadlock detection on the wait-for graph andonly aborts a transaction if it finds a cycle in the graph. To meet agoal of not utilizing a centralized transaction manager, eachtransaction (e.g., executing on a given execution node) are able toexchange wait-for information and perform deadlock detectionindependently. Further, the subject technology implements a deadlockdetection algorithm that is deterministic so that all transactions canunanimously agree on which transactions to abort.

In the following discussion, it is understood that statements in atransaction are executed serially e.g., one at a time. As discussedfurther below, the subject technology can then extend a deadlockdetection protocol as described herein to support parallel statementexecution.

In the discussion below, “transaction” and “statement” are usedinterchangeably because it is assumed that statements of a transactionwill be executed serially, e.g., one at a time. In an example, thesubject technology utilizes a deadlock detection and resolution protocolthat enables transactions to store their wait-for information into adedicated table in a distributed database (e.g., storage platform 104).A transaction waiting for conflicting transactions can periodically runa deterministic deadlock detection algorithm. If a transactiondetermines that it is a victim in a deadlock, the transaction can abortitself so that other transactions can proceed.

In some implementations, the execution platform 110 can provide deadlockhandling logic 480 (e.g., deadlock handling logic l to deadlock handlinglogic N, which can correspond respectively to each transaction 420 totransaction 425) which implements the deadlock detection and resolutionprotocol mentioned herein, and is provided or utilized by each givenexecution node that is currently executing a given transaction(s). Inanother embodiment, each deadlock handling logic can be provided to acorresponding transaction (or statement within a transaction) fordeadlock detection and resolution as described further herein.

In an embodiment, wait-for information of transactions is stored in await-for table in the distributed database (e.g., storage platform 104).The wait-for table includes a set of key-value pairs where both keys andvalues are transaction IDs. A key-value pair <Ti, Tj> means that Ti iscurrently waiting for Tj, e.g., there is an edge Ti→Tj in the wait-forgraph.

In order to satisfy the deterministic requirement, each transaction Tireports Ti→Tj only if Tj is the oldest conflicting transaction that Tiis waiting for (a transaction's age is determined by its transaction ID,e.g., a younger (e.g., newer) transaction will have a larger transactionID). By ensuring that there is at most one ongoing edge from eachtransaction, it is straightforward to see that each transaction canparticipate in at most one cycle. Thus, the youngest transaction (withthe largest transaction ID) can be aborted in each cycle todeterministically resolve deadlocks.

FIG. 5 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 500 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 500 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 500 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 500 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

At operation 502, the transaction manager 440 receives a firsttransaction, the first transaction to be executed on linearizablestorage.

At operation 504, the transaction manager 440 assigns a first readversion to the first transaction, the first read version indicating afirst version of the linearizable storage. Alternatively, a readtimestamp can be retrieved from a clock service (e.g., the clock service130), and a transaction identifier can be assigned to the firsttransaction where the transaction identifier corresponds to a read starttime.

At operation 506, the transaction manager 440 performs a read operationfrom the first transaction on a table in a database.

At operation 508, the transaction manager 440 determines a first commitversion identifier corresponding to first data resulting from the readoperation.

At operation 510, the transaction manager 440 determines whether aparticular write operation is included in the first transaction. If theparticular write operation is to be performed with the firsttransaction, then the transaction manager 440 proceeds to perform amethod as described below in FIG. 7.

Alternatively, when the transaction manager 440 determines that aparticular write operation is absent from the first transaction, atoperation 512, the transaction manager 440 proceeds to execute adifferent transaction (along with foregoing to perform a commit processfor the first transaction), which is described, in an example, in FIG. 6below. It is appreciated that due to the concurrency of transactionsthat are performed, the operations described further below in FIG. 6 canbe executed at any time during the operations described in FIG. 5 above.

FIG. 6 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 600 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 600 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 600 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 600 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

In some embodiments, the method 600 can be performed in conjunction withthe method 500 as discussed above. For example, the method 600 can beperformed after the operations of the method 500 or performedsubstantially concurrently with the method 500.

At operation 602, the transaction manager 440 receives a secondtransaction, the second transaction to be executed on linearizablestorage.

At operation 604, the transaction manager 440 assigns the secondtransaction a second read version, the second read version indicating asecond version of the linearizable storage.

At operation 606, the transaction manager 440 performs a second readoperation from the second transaction on the table in the database.

At operation 608, the transaction manager 440 performs a second writeoperation from the second transaction on the table in the database.

At operation 610, the transaction manager 440 determines a particularcommit version identifier corresponding to second data results from thesecond read operation.

At operation 612, the transaction manager 440 completes the writeoperation in response to the particular commit version identifier beingequivalent to the first commit version identifier.

At operation 614, the transaction manager 440 assigns a second commitversion identifier to second data stored to the table from the writeoperation, the second commit version identifier corresponding to asecond version of data in the table, the second commit versionidentifier different than the first commit version identifier.

At operation 616, the transaction manager 440 initiates a commit processfor the second transaction.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 700 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 700 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 700 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 700 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

In some embodiments, the method 700 can be performed in conjunction withthe method 500 and the method 600 as discussed above. For example, themethod 700 can be performed after the operations of the method 500 orthe method 600 (or performed substantially concurrently therewith eithermethod).

At operation 702, the transaction manager 440 proceeds to perform aparticular write operation from the first transaction.

At operation 704, the transaction manager 440 determines that the firstcommit version identifier fails to match the second commit versionidentifier.

At operation 706, the transaction manager 440 aborts the particularwrite operation from the first transaction.

At operation 708, the transaction manager 440 performs a particular readoperation from the first transaction on the table in the database.

At operation 710, the transaction manager 440 determines a particularcommit version identifier corresponding to particular data resultingfrom the particular read operation.

At operation 712, the transaction manager 440 retry to perform theparticular write operation from the first transaction.

At operation 714, the transaction manager 440 perform the particularwrite operation in response to the particular commit version identifiermatching the second commit version identifier

At operation 716, the transaction manager 440 initiates a particularcommit process for the first transaction.

FIG. 8 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 800 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 800 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 800 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 800 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

Continuing the discussion of handling deadlocks (e.g., detection andresolution) in a distributed manner as described previously in FIG. 4, adeadlock detection protocol in accordance with some embodiments isdiscussed in the following. As described before, a given transaction canbe assigned to and executed by a particular execution node from theexecution platform 110. For the purposes of explanation below, suppose astatement from a transaction Ti attempts to lock a set of keys S at 802.

-   1) If Ti has no conflicting transaction at 804, then Ti can proceed    safely at 806.-   2) Otherwise, suppose there are some conflicting transactions T1 . .    . Tn. At 808, Ti waits for these conflicting transactions one by    one, from T1 to Tn. In this example, let T1 be the oldest    transaction that Ti is currently waiting for.-   3) If T1 finishes at 810 within time period P, then Ti proceeds to    wait for the next conflicting transaction at 818.    -   a) Ti removes Ti→T1 from the wait-for table if Ti has performed        deadlock detection-   4) Otherwise, Ti performs deadlock detection at 812 as follows:    -   a) Ti publishes Ti→T1 to the wait-for table    -   b) Ti performs cycle detection starting from Ti        -   i) If Ti belongs to a cycle and Ti is the youngest            transaction in the cycle at 814, Ti is aborted at 816        -   ii) Otherwise, Ti goes back to 808 and waits P seconds or            until T1 terminates-   5) After all conflicting transactions of Ti have completed, Ti    restarts the current statement at 820.

In some embodiments, the wait-for table is stored in distributeddatabase as a Key-Value Table with a reserved, special name, and bemaintained per-account, similarly to the Transaction Status Table. Thekey and value format can be:

-   -   (account ID, wait-for table name, Ti)=(statement num, restart        count, Tj)    -   A key value pair <Ti, statement num, restart count, Tj> means        that a statement with the given restart count of transaction Ti        is currently waiting for Tj. A transaction Ti adds a key-value        pair <Ti, (statement num, restart count, Tj)> to the wait-for        table if Tj is the oldest conflicting transaction of Ti. When Tj        completes or Ti aborts, the statement that publishes this        information will be responsible for removing it from the        wait-for table.

In some embodiments, any crashed transaction is eventually aborted orretried and committed. When a crashed transaction restarts, thestatement could clean up the wait-for table to remove any leftoverwait-for information published by this statement.

In some embodiments, a transaction waits for its conflictingtransactions before it can safely restart the current statement orinitiate deadlock detection. The transaction status table (TST) storesthe status of each transaction, i.e., COMMITTED, ABORTED or PENDING. Inaddition, distributed database supports watches that allow a caller tobe notified once the value of a given key changes. Thus, waiting forconflicting transactions can be implemented using watches as discussedin the following.

Suppose a statement S of a transaction Ti has a list of conflictingtransactions T1 to Tn. STi waits for the oldest conflicting transactionT1 transaction to commit or abort using the following KeyValueStore API:

-   -   future<void> KeyValueStore::waitUntil(Key key,        bool(optional<Value>) pred);

KeyValueStore::waitUntil(key, pred) returns a future that is completedafter pred evaluates to true. In an example, KeyValueStore::waitUntilcan be implemented using distributed database watches based on thefollowing:

-   -   future<bool> KeyValueStore::conditionalWatch(Key key,        bool(optional<Value>) pred);

KeyValueStore::conditionalWatch conditionally registers a watch if predevaluates to true. It returns a future with a Boolean value to indicatewhether a watch is created and the future is returned if the watch isnot created or the watch has been fired.

Based on KeyValueStore::conditionalWatch, KeyValueStore::waitUntil(key,pred) can be implemented as follows:

-   -   Call KeyValueStore::watch(key, !pred) and wait for its        completion. If the return value is false, then notify the caller        directly.    -   Otherwise, call KeyValueStore::watch(key, !pred) again until the        return value becomes false.

Ti then waits for the returned future of KeyValueStore::waitUntil(key,pred) to complete for some time window P. If T1 completes, then Sproceeds to wait for the next conflicting transaction T2. Otherwise, Sinitiates deadlock detection as discussed below.

In some embodiments, deadlock detection is implemented using thewait-for table. In an example, suppose statement S of Ti's oldestconflicting transaction is T1. During deadlock detection, statement Sfirst writes <Ti, (S, restart count, T1)> to the wait-for table, if T1is the oldest conflicting transaction of Ti. S then issues a series ofdistributed database point reads to perform graph traversal startingfrom Ti. For example, if there exists <Ti, T1> in the wait-for table, Tiwill continue the traversal by reading T1 and so on. Eventually, therecan be four outcomes:

-   -   1. No cycle (the last transaction Tj does not exist in the        wait-for table).    -   2. There is a cycle but Ti is not a part of it (the last        transaction Tj has been traversed before, but Ti!=Tj).    -   3. There is a cycle involving Ti but Ti is not the youngest        transaction in the cycle (the last transaction Tj==Ti).    -   4. There is a cycle involving Ti and Ti is the youngest        transaction in the cycle (the last traversed transaction        Ti==Tj).

In an example, S continues to wait (for another time period P) for cases1-3 above. S aborts Ti for example 4 above.

Once a transaction Ti determines that it is the victim of a deadlock,the subject technology will returns a statement indicating a deadlock(e.g., StatementOutcome::DEADLOCK) to an execution node (or to theexecution platform 110, or a client, or transaction manager 440) thatattempts to finalize the current statement. The execution node will thenbe responsible for:

-   -   Abort the transaction. This is implemented by updating TST.Ti as        ABORTED so that all previously held locks of Ti are released.    -   Propagate the deadlock error, which may eventually be returned        to the user.

The subject technology may additionally include the cycle information inthe error message to help the user eliminate the deadlock.

As mentioned above, the protocol described above (e.g., FIG. 8) may notbe applicable to a scenario when a transaction executes multiplestatements in parallel. To help illustrate this problem, assume thattransaction T0 has two statements S1 and S2. In this example, S1 iscurrently waiting for transaction T,1 and S2 is currently waiting fortransaction T2. Both transactions T1 and T2 are waiting for T1. When T1is performing deadlock detection, T0.S2 could have already updated thewait-for information as T0→T2 and thus T1 will not abort itself in thisinstance. Similarly, when T2 is performing deadlock detection, T0.S1could have already updated the wait-for information as T0→T1 and thus T2will not abort itself in this instance. This example scenario isproblematic because the deadlock may never be resolved.

In the above example, a reason why the protocol may not resolve thedeadlock for parallel statements is that the invariant that atransaction only publishes its oldest conflicting transaction to thewait-for table may no longer hold because multiple statements of atransaction can modify the wait-for table concurrently. In order toaddress this problem where transactions can execute in parallel andextend the protocol to support such parallel transactions, in someembodiments, the deadlock detection algorithm can be extended so that astatement of a transaction Ti will only publish Ti→Tj into the wait-fortable if:

-   -   1. Ti is currently not waiting for any conflicting transaction        in the wait-for table, or    -   2. There exists Ti→Tk in the wait-for table and Tj is older than        Tk.        Moreover, after performing deadlock detection, a statement will        only remove the wait-for information from the wait-for table if        this information is published by this statement.

FIG. 9 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 900 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 900 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 900 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 900 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

In the example of FIG. 9, a particular execution node (e.g., executionnode 302-1), utilizing in part deadlock handling logic 480, performs thefollowing operations. It is appreciated, however, that other componentsof the subject system can perform the following operations in someembodiments.

At operation 902, the execution node 302-1 performs a locking operationon a first set of keys by a first statement of a first transaction.

At operation 904, the execution node 302-1 determines that a conflictoccurred between the first statement and a second transaction.

At operation 906, the execution node 302-1 determines that the secondtransaction has yet to complete after a predetermined period of time.

At operation 908, the execution node 302-1 performs a deadlock detectionprocess, the performing including the following operations. At operation910, the execution node 302-1 stores a key and value in a tableindicating the first transaction and the second transaction. Atoperation 912, the execution node 302-1 detects, based at least in parton a graph traversal of the table starting from the first transaction, acycle between the first transaction and the second transaction. Atoperation 914, the execution node 302-1 determines that the firsttransaction is the youngest transaction in the detected cycle.

In some embodiments, performing the deadlock detection process caninclude the following operations. The execution node 302-1 determinesthat no cycle is indicated in the table, or determines that a particularcycle is indicated in the table and the first transaction is notassociated with the particular cycle, or determines that a second cycleis indicated in the table and the first transaction is not the youngesttransaction in the second cycle. Further, execution node 302-1 stores akey value pair in the table, the key value pair comprising a key and avalue, the key indicates the first transaction and the value indicatesthe second transaction. In an example, the value further includes astatement number and a restart count.

In some embodiments, performing the deadlock detection process caninclude the following operations when transactions are executing inparallel in the subject system.

The execution node 302-1 determines that the first transaction is notwaiting for a conflicting transaction, the conflicting transactioncomprising a particular transaction that has requested a lock on thefirst set of keys prior to the first transaction performing the lockingoperation. For example, determining that the first transaction is notwaiting for a conflicting transaction is based at least in part on notlocating information in the table indicating a key value pair, the keyvalue pair including a key corresponding to the first transaction and avalue corresponding to the conflicting transaction.

Moreover, execution node 302-1 determines, in a graph representation ofthe table, that an edge between the first transaction and the particulartransaction is indicated in the table and that the conflictingtransaction is older than the particular transaction, the edgecorresponding to a key value pair in the table, the key value paircomprising a key and a value, the key indicating the first transactionand the value indicating the second transaction.

The execution node 302-1, in response to determining that the firsttransaction is not waiting for the conflicting transaction, ordetermining that the edge between the first transaction and theparticular transaction is indicated in the table and that theconflicting transaction is older than the particular transaction:execution node 302-1 stores, in the table, a particular key value paircorresponding to the first transaction and the conflicting transaction,the particular key value pair indicating an edge between the firsttransaction and the conflicting transaction in the graph representationof the table.

Further, execution node 302-1 determines that the conflictingtransaction has completed executing; and removes the particular keyvalue pair from the table.

At operation 916, the execution node 302-1 ceases execution of the firsttransaction in response to the first transaction being the youngesttransaction in the detected cycle.

FIG. 10 illustrates a diagrammatic representation of a machine 1000 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1000 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1016 may cause the machine 1000 to execute anyone or more operations of method 500, method 600, method 700, method800, and method 900. As another example, the instructions 1016 may causethe machine 1000 to implement portions of the data flows illustrated inat least FIG. 4. In this way, the instructions 1016 transform a general,non-programmed machine into a particular machine 1000 (e.g., the computeservice manager 108 or a node in the execution platform 110) that isspecially configured to carry out any one of the described andillustrated functions in the manner described herein.

In alternative embodiments, the machine 1000 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1000 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1016, sequentially orotherwise, that specify actions to be taken by the machine 1000.Further, while only a single machine 1000 is illustrated, the term“machine” shall also be taken to include a collection of machines 1000that individually or jointly execute the instructions 1016 to performany one or more of the methodologies discussed herein.

The machine 1000 includes processors 1010, memory 1030, and input/output(I/O) components 1050 configured to communicate with each other such asvia a bus 1002. In an example embodiment, the processors 1010 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1012 and aprocessor 1014 that may execute the instructions 1016. The term“processor” is intended to include multi-core processors 1010 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1016 contemporaneously. AlthoughFIG. 10 shows multiple processors 1010, the machine 1000 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1030 may include a main memory 1032, a static memory 1034,and a storage unit 1036, all accessible to the processors 1010 such asvia the bus 1002. The main memory 1032, the static memory 1034, and thestorage unit 1036 store the instructions 1016 embodying any one or moreof the methodologies or functions described herein. The instructions1016 may also reside, completely or partially, within the main memory1032, within the static memory 1034, within machine storage medium 1038of the storage unit 1036, within at least one of the processors 1010(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1050 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1050 thatare included in a particular machine 1000 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1050 mayinclude many other components that are not shown in FIG. 10. The I/Ocomponents 1050 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via a coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor another suitable device to interface with the network 1080. Infurther examples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1070 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1000 may correspond to any one ofthe compute service manager 108 or the execution platform 110, and thedevices 1070 may include the client device 114 or any other computingdevice described herein as being in communication with the network-baseddatabase system 102 or the cloud storage platform 104.

Executable Instructions and Machine Storage Medium

The various memories (e.g., 1030, 1032, 1034, and/or memory of theprocessor(s) 1010 and/or the storage unit 1036) may store one or moresets of instructions 1016 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1016, when executed by theprocessor(s) 1010, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple non-transitory storage devices and/or non-transitory media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store executable instructions and/or data. The termsshall accordingly be taken to include, but not be limited to,solid-state memories, and optical and magnetic media, including memoryinternal or external to processors. Specific examples of machine-storagemedia, computer-storage media, and/or device-storage media includenon-volatile memory, including by way of example semiconductor memorydevices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM),field-programmable gate arrays (FPGAs), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium” discussed below.

Transmission Medium

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1080 or a portion of the network1080 may include a wireless or cellular network, and the coupling 1082may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1082 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1016 may be transmitted or received using a transmission medium via thecoupling 1072 (e.g., a peer-to-peer coupling) to the devices 1070. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1016 for execution by the machine 1000, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the method 500 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

CONCLUSION

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardwareprocessor; and a memory storing instructions that cause the at least onehardware processor to perform operations comprising: performing alocking operation on a first set of keys by a first statement of a firsttransaction; determining that a conflict occurred between the firststatement and a second transaction; determining that the secondtransaction has yet to complete after a predetermined period of time;performing a deadlock detection process, the performing comprising:storing a key value pair in a table, the key value pair comprising a keyand a value, wherein the key indicates the first transaction and thevalue indicates the second transaction, wherein the value furtherincludes a statement number and a restart count; detecting, based atleast in part on a graph traversal of a table starting from the firsttransaction, a cycle between the first transaction and the secondtransaction; determining that the first transaction is a youngesttransaction in the detected cycle; and ceasing execution of the firsttransaction in response to the first transaction being the youngesttransaction in the detected cycle.
 2. The system of claim 1, whereinperforming the deadlock detection process further comprises: determiningthat no cycle is indicated in the table, or determining that aparticular cycle is indicated in the table and the first transaction isnot associated with the particular cycle, or determining that a secondcycle is indicated in the table and the first transaction is not theyoungest transaction in the second cycle.
 3. The system of claim 1,wherein the first transaction and the second transaction are executingin parallel and performing the deadlock detection process furthercomprises: determining that the first transaction is not waiting for aconflicting transaction, the conflicting transaction comprising aparticular transaction that has requested a lock on the first set ofkeys prior to the first transaction performing the locking operation. 4.The system of claim 3, wherein determining that the first transaction isnot waiting for a conflicting transaction is based at least in part onnot locating information in the table indicating a key value pair, thekey value pair including a key corresponding to the first transactionand a value corresponding to the conflicting transaction.
 5. The systemof claim 3, wherein the operations further comprise: determining, in agraph representation of the table, that an edge between the firsttransaction and the particular transaction is indicated in the table andthat the conflicting transaction is older than the particulartransaction, the edge corresponding to a key value pair in the table,the key value pair comprising a key and a value, the key indicating thefirst transaction and the value indicating the second transaction. 6.The system of claim 5, wherein the operations further comprise: inresponse to determining that the first transaction is not waiting forthe conflicting transaction, or determining that the edge between thefirst transaction and the particular transaction is indicated in thetable and that the conflicting transaction is older than the particulartransaction: storing, in the table, a particular key value paircorresponding to the first transaction and the conflicting transaction,the particular key value pair indicating an edge between the firsttransaction and the conflicting transaction in the graph representationof the table.
 7. The system of claim 6, wherein the operations furthercomprising: determining that the conflicting transaction has completedexecuting; and removing the particular key value pair from the table. 8.A method comprising: performing a locking operation on a first set ofkeys by a first statement of a first transaction; determining that aconflict occurred between the first statement and a second transaction;determining that the second transaction has yet to complete after apredetermined period of time; performing a deadlock detection process,the performing comprising: storing a key value pair in a table, the keyvalue pair comprising a key and a value, wherein the key indicates thefirst transaction and the value indicates the second transaction,wherein the value further includes a statement number and a restartcount; detecting, based at least in part on a graph traversal of a tablestarting from the first transaction, a cycle between the firsttransaction and the second transaction; determining that the firsttransaction is a youngest transaction in the detected cycle; and ceasingexecution of the first transaction in response to the first transactionbeing the youngest transaction in the detected cycle.
 9. The method ofclaim 8, wherein performing the deadlock detection process furthercomprises: determining that no cycle is indicated in the table, ordetermining that a particular cycle is indicated in the table and thefirst transaction is not associated with the particular cycle, ordetermining that a second cycle is indicated in the table and the firsttransaction is not the youngest transaction in the second cycle.
 10. Themethod of claim 8, wherein the first transaction and the secondtransaction are executing in parallel and performing the deadlockdetection process further comprises: determining that the firsttransaction is not waiting for a conflicting transaction, theconflicting transaction comprising a particular transaction that hasrequested a lock on the first set of keys prior to the first transactionperforming the locking operation.
 11. The method of claim 10, whereindetermining that the first transaction is not waiting for a conflictingtransaction is based at least in part on not locating information in thetable indicating a key value pair, the key value pair including a keycorresponding to the first transaction and a value corresponding to theconflicting transaction.
 12. The method of claim 10, further comprising:determining, in a graph representation of the table, that an edgebetween the first transaction and the particular transaction isindicated in the table and that the conflicting transaction is olderthan the particular transaction, the edge corresponding to a key valuepair in the table, the key value pair comprising a key and a value, thekey indicating the first transaction and the value indicating the secondtransaction.
 13. The method of claim 12, further comprising: in responseto determining that the first transaction is not waiting for theconflicting transaction, or determining that the edge between the firsttransaction and the particular transaction is indicated in the table andthat the conflicting transaction is older than the particulartransaction: storing, in the table, a particular key value paircorresponding to the first transaction and the conflicting transaction,the particular key value pair indicating an edge between the firsttransaction and the conflicting transaction in the graph representationof the table.
 14. The method of claim 13, further comprising:determining that the conflicting transaction has completed executing;and removing the particular key value pair from the table.
 15. Anon-transitory computer-storage medium comprising instructions that,when executed by one or more processors of a machine, configure themachine to perform operations comprising: performing a locking operationon a first set of keys by a first statement of a first transaction;determining that a conflict occurred between the first statement and asecond transaction; determining that the second transaction has yet tocomplete after a predetermined period of time; performing a deadlockdetection process, the performing comprising: storing a key value pairin a table, the key value pair comprising a key and a value, wherein thekey indicates the first transaction and the value indicates the secondtransaction, wherein the value further includes a statement number and arestart count; detecting, based at least in part on a graph traversal ofa table starting from the first transaction, a cycle between the firsttransaction and the second transaction; determining that the firsttransaction is a youngest transaction in the detected cycle; and ceasingexecution of the first transaction in response to the first transactionbeing the youngest transaction in the detected cycle.
 16. Thenon-transitory computer-storage medium of claim 15, wherein performingthe deadlock detection process further comprises: determining that nocycle is indicated in the table, or determining that a particular cycleis indicated in the table and the first transaction is not associatedwith the particular cycle, or determining that a second cycle isindicated in the table and the first transaction is not the youngesttransaction in the second cycle.
 17. The non-transitory computer-storagemedium of claim 15, wherein the first transaction and the secondtransaction are executing in parallel and performing the deadlockdetection process further comprises: determining that the firsttransaction is not waiting for a conflicting transaction, theconflicting transaction comprising a particular transaction that hasrequested a lock on the first set of keys prior to the first transactionperforming the locking operation.
 18. The non-transitorycomputer-storage medium of claim 17, wherein determining that the firsttransaction is not waiting for a conflicting transaction is based atleast in part on not locating information in the table indicating a keyvalue pair, the key value pair including a key corresponding to thefirst transaction and a value corresponding to the conflictingtransaction.
 19. The non-transitory computer-storage medium of claim 17,wherein the operations further comprise: determining, in a graphrepresentation of the table, that an edge between the first transactionand the particular transaction is indicated in the table and that theconflicting transaction is older than the particular transaction, theedge corresponding to a key value pair in the table, the key value paircomprising a key and a value, the key indicating the first transactionand the value indicating the second transaction.
 20. The non-transitorycomputer-storage medium of claim 19, wherein the operations furthercomprise: in response to determining that the first transaction is notwaiting for the conflicting transaction, or determining that the edgebetween the first transaction and the particular transaction isindicated in the table and that the conflicting transaction is olderthan the particular transaction: storing, in the table, a particular keyvalue pair corresponding to the first transaction and the conflictingtransaction, the particular key value pair indicating an edge betweenthe first transaction and the conflicting transaction in the graphrepresentation of the table.
 21. The non-transitory computer-storagemedium of claim 20, wherein the operations further comprising:determining that the conflicting transaction has completed executing;and removing the particular key value pair from the table.